Streamflow Measurements Research Articles

140-year daily ensemble streamflow reconstructions over 661 catchments in France

Abstract. The recent development of FYRE (French hYdroclimate REanalysis) Climate, a high-resolution ensemble daily reanalysis of precipitation and temperature covering the 1871–2012 period and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J (Génie Rural à 6 Paramètres Journaliers) lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions, HydRE (Hydrological REconstruction), spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty using forcings from all 25 ensemble members provided by FYRE Climate and (2) the streamflow measurement error by perturbing observations used during the calibration. Further, the hydrological model error based on the relative discrepancies between observed and simulated streamflow has been added to derive the HydREM (Hydrological REconstruction with Model error) streamflow reconstructions. These two reconstructions are compared to other hydrological reconstructions with different meteorological inputs, hydrological reconstructions from a machine learning algorithm, and independent and dependent observations. Overall, the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing us to better understand the variability in past hydrology over France.

Streamflow seasonality in a snow-dwindling world.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

Identification of deep Czech Republic–Austria transboundary aquifer discharge and associated river chloride loading

The deep transboundary aquifer of regional scale along the Czech Republic–Austria border in Central Europe serves as a thermal-mineral water resource for balneotherapy and plays an important role in the region’s development. The aquifer is composed mostly of Jurassic carbonates at depths from 160 to − 3000 masl. Despite more than two decades of exploitation, no complex analysis of groundwater flow directions and groundwater fluxes ever took place. Now, cross-border cooperation enabled the research team to gather crucial information on the Jurassic aquifer. For a better understanding of the groundwater flow system, a numerical model was developed. To simulate the effect of variable density and viscosity occurring in such a deep aquifer, the SEAWAT numerical model was used. The simulation shows that there is an inflow of low mineralised groundwater from the crystalline outcrops in the northwest and inflow of saline groundwater from southeast. Aquifer discharge was identified along the zone partly corresponding to the course of the Dyje River. To check the model’s accuracy, the river water was sampled together with streamflow measurements. Detected sections of increasing chloride concentration indicate zones of the Jurassic aquifer discharge into the Dyje River. The discharge rate of 85 L/s derived from streamflow and chloride concentrations matches the value computed by the model. The relatively high discharge of the Jurassic aquifer contributes significantly to the high chloride loading observed in the Dyje River.

Stream hydrology controls on ice cliff evolution and survival on debris-covered glaciers

Abstract. Ice cliffs are melt hot spots that contribute disproportionately to melt on debris-covered glaciers. In this study, we investigate the impact of supraglacial stream hydrology on ice cliffs using in situ and remote sensing observations, streamflow measurements, and a conceptual geomorphic model of ice cliff backwasting applied to ice cliffs on Kennicott Glacier, Alaska. We found that 33 % of ice cliffs (accounting for 69 % of the ice cliff area) are actively influenced by streams, while half are nearer than 10 m from the nearest stream. Supraglacial streams contribute to ice cliff formation and maintenance by horizontal meandering, vertical incision, and debris transport. These processes produce an undercut lip at the ice cliff base and transport clasts up to tens of centimeters in diameter, preventing reburial of ice cliffs by debris. Stream meander morphology reminiscent of sedimentary river channel meanders and oxbow lakes produces sinuous and crescent ice cliff shapes. Stream avulsions result in rapid ice cliff collapse and local channel abandonment. Ice cliffs abandoned by streams are observed to be reburied by supraglacial debris, indicating a strong role played by streams in ice cliff persistence. We also report on a localized surge-like event at the glacier's western margin which drove the formation of ice cliffs from crevassing; these cliffs occur in sets with parallel linear morphologies contrasting with the crescent planform shape of stream-driven cliffs. The development of landscape evolution models may assist in quantifying the total net effect of these processes on steady-state ice cliff coverage and mass balance, contextualizing them with other drivers including supraglacial ponds, differential melt, ice dynamics, and collapse of englacial voids.

Effect of streamflow measurement error on flood frequency estimation

Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km2. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors.

Modeling pesticides and ecotoxicological risk assessment in an intermittent river using SWAT

The present work aimed to predict the fate of two pesticides, copper (Cu) and glyphosate in a Mediterranean basin with an intermittent river and to assess the ecotoxicological risk related to their presence in water bodies coupling field measurements of streamflow and pesticide concentrations, and an eco-hydrological model. The Soil and Water Assessment Tool (SWAT) model was calibrated and, subsequently used to assess predicted environmental concentrations of pesticides in surface waters. The ecotoxicological risk related to the presence of Cu and glyphosate in surface water was assessed at the reach scale by using the Toxicity to Exposure Ratio approach (TER). Measurements of glyphosate concentrations (< 0.5 μg l−1) exceeded the maximum European threshold of environmental quality standards for pesticides (EQS) of 0.1 μg l−1. High concentrations of glyphosate were predicted in the wet season and in September, when glyphosate is mostly used in vineyards and olive grove productions. Acute risk (TER < 100) associated with the presence of glyphosate was detected for several reaches. High concentrations of Cu (< 6.5 μg l−1), mainly used as a fungicide in vineyards, were predicted in several river reaches. The results of the ecotoxicological risk assessment revealed that November and January were the critical months during which most of the river reaches showed a chronic risk associated with the presence of Cu.

Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records

Abstract. Quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalies related to sensor drift, gauge movement, and incomplete rating curves. Here, we investigate the potential to estimate continuous discharge when discrete streamflow measurements are available at the site of interest. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Reconstructions achieved median efficiencies of 0.83 (Nash–Sutcliffe, or NSE) and 0.81 (Kling–Gupta, or KGE) across all sites and improved KGE at 11 sites versus published data, with linear regression generally outperforming deep learning approaches due to the use of target site data for model fitting rather than evaluation only. Estimates from this analysis inform ∼199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON's stream data products. We provide 5 min composite discharge series for each site that combine the best estimates across modeling approaches and NEON's published data. The success of this effort demonstrates the potential to establish “virtual gauges”, sites at which continuous streamflow can be accurately estimated from discrete measurements, by transferring information from nearby donor gauges and/or large collections of training data.

Advanced Quantitative Precipitation Information: Improving Monitoring and Forecasts of Precipitation, Streamflow, and Coastal Flooding in the San Francisco Bay Area

Abstract Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide.

On the performance of streamflow gauging using CCTV-integrated LSPIV in diverse hydro-environmental conditions.

Addressing the critical need for precise streamflow measurements in hydro-environmental research, this study evaluates large-scale particle image velocimetry (LSPIV) using cost-effective closed-circuit television (CCTV) cameras, providing a detailed sensitivity analysis in both laboratory and real-world canal settings. In laboratory conditions, a 45° camera angle notably enhanced performance, exhibiting a 12% decrease in MAE and a remarkable 40% reduction in RMSE compared to the performance of orthographic form. Tracer particles further enhanced LSPIV accuracy, decreasing both mean absolute error (MAE) and root mean square error (RMSE) by around 0.05 m/s. Optimal velocity coefficients for the lab ranged between 0.85 and 0.90. Nighttime measurements, using projection-based illumination, showed a minor 3% MAE variation and 0.02 RMSE difference versus daytime. In field experiments, a 45° upstream CCTV camera configuration notably improved LSPIV accuracy, achieving a 3% MAE and 0.055 m/s RMSE. For best results across different turbidity levels, we recommend a velocity coefficient range of 0.84 to 0.88. This study highlights the robustness and cost-efficiency of LSPIV as a transformative method for streamflow gauging, demonstrating its wide applicability in diverse hydro-environmental scenarios.

GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present

Abstract. Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).

Integrating multi-criteria decision analysis (MCDA) with kriging and entropy methods for optimising streamflow measurement in a scantly monitored river basin

ABSTRACT Streamflow data is one of the most important inputs for the management of water resources in river basins. In this study, a GIS-based multi-criteria decision analysis (MCDA) was integrated with kriging and entropy methods for optimising a streamflow monitoring network in the upper Tekeze River basin, north-western Ethiopia. An initial evaluation based on the WMO (2008) guideline showed that the density of existing streamflow monitoring network for the upper Tekeze River basin was inadequate. Hence, 41 new streamflow monitoring stations were needed. The most important criteria for identifying suitable streamflow monitoring sites in the upper Tekeze River basin were stream order, accessibility (distance to roads), slope gradient, land use/cover and soil texture. The required number of new streamflow monitoring stations for the upper Tekeze River basin was reduced from 41 to 33 after an optimisation using ordinary kriging and entropy. The optimal number of monitoring stations for the upper Tekeze River basin including the existing ones was therefore 47. An integrated application of MCDA, kriging and entropy methods aids to optimise the location and number of streamflow monitoring stations in a catchment. The use of optimisation techniques for designing streamflow monitoring network is essential for collecting adequate streamflow data in scantly monitored basins.

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

Greenup Variability Impact on Seasonal Streamflow and Soil Moisture Dynamics in Humid, Temperate Forests

AbstractIn this study, we investigate how seasonal streamflow and soil moisture patterns have responded to variability in vegetation phenology in humid, temperate forested watersheds without significant seasonal snowmelt over the last four decades. We characterize spring streamflow peaks using 50th percentiles of cumulative daily precipitation, streamflow, and soil moisture measurements, and investigate interactions with remotely sensed, greenup anomalies. After removing a dominant precipitation control, 1‐day earlier greenup is usually associated with about 1‐day early spring flow peak at four low‐elevation deciduous catchments using both sequential and multiple linear regressions. This indicates that the strong dependency of seasonal flow regimes on precipitation is mediated by vegetation seasonality, especially by greenup variability. In contrast, we find less significant correlations of the greenup anomalies on flow percentiles from two paired evergreen and two high‐elevation deciduous catchments. At a plot scale, similar correlations were found only at an upslope topographic position, where precipitation also showed tighter coupling with moisture seasonal patterns than downslope. Our study suggests that rainfall‐runoff and rainfall‐soil moisture relations have been closely mediated by vegetation seasonality in deciduous forests, especially by greenup anomalies, but patterned along topoclimate and hillslope gradients. This study emphasizes that it is important to understand phenological responses to ongoing climate change (in both long‐term and interannual variability) for prediction of seasonal flow regimes especially in deciduous forested catchments.

Hydrological simulation of ungauged basins via forcing by large‐scale hydrology models

AbstractThe established watershed's hydrologic simulation process, involving the calibration and validation of a model through spatiotemporal streamflow observations, has recently been adapted to encompass ungauged watersheds. The research introduces a methodology for simulating ungauged hydrosystems, suggesting the calibration and validation of regional rainfall‐runoff models using large‐scale hydrological models (LSMs) in the absence of in situ streamflow measurements. For doing so, LSM's 30‐years simulated discharges at various basins and subbasins of the case study River Basin District are assessed for their accuracy against the limited available observations. Subsequently, the rectified time series tunes the optimal parametrization of a regional lumped hydrologic model that is triggered by precipitation and temperature data from a partially dense meteorological network. The validated regional model is then applied in selective ungauged neighbouring subbasins employing the regionalization concept used in LSMs, that is in basins with similar physiographic characteristics to those where the regional model was set up. The performance of the research is assessed using statistical metrics. In cases where observations are available, the results show very satisfactory indexes (e.g., coefficient of determination: 0.654 &lt; R2 &lt; 0.949, Kling‐Gupta efficiency: 0.759 &lt; KGE &lt;0.790, Nash–Sutcliffe efficiency: 0.649 &lt; NSE &lt;0.745 for all the cases). In completely ungauged basins and in an order of magnitude index, the comparison of the simulated discharges with those from the LSM also indicates the satisfactory performance of the proposed approach. To sum up, the methodology underscores the alternate use of large and regional scale hydrological modelling coupling as an asset across various basin scales globally, offering reliable water quantity information.

Uncertainty in streamflow measurements significantly impacts estimates of downstream nitrate export

Across watershed science, two key variables emerge–streamflow and solute concentration–which serve as the basis for efforts ranging from basic watershed biogeochemistry research to policy decisions surrounding watershed management. However, we rarely account for how error in discharge (Q) impacts estimates of downstream nutrient loading. Here, we examined the impact of uncertainty in streamflow measurements on estimates of downstream nitrate export using publicly available data from the U.S. Geological Survey (USGS). We characterized how uncertainty in stage-discharge relationships impacts annual flux estimates across 70 USGS gages. Our results indicate the interquartile range of relative error in Q was 33% across these USGS sites. We documented a wide range in mean error in annual nitrate loads; some sites were underestimated (−105%), while predicted loads at other sites vastly overestimated (500%). Overall, any error in estimating Q leads to significant unpredictability of annual nutrient loads, which are often used as critical success benchmarks for governmental nutrient reduction strategies. Moreover, error in annual nitrate loads (as mass, kg) increases with mean Q; thus, as high flows become more unpredictable and intense under future climate change, error in estimates of downstream nutrient loading may also increase. Together, this indicates that error in Q may drastically influence our measures of water quality success and decrease our ability to accurately quantify progress towards algal bloom and ‘dead zone’ reduction.

Comparing the ability of different remotely sensed evapotranspiration products in enhancing hydrological model performance and reducing prediction uncertainty

The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.

A multi-tracer approach to quantifying groundwater discharge in flat areas

Identifying of groundwater discharge in surface waters is of outstanding interest in order to complete water balance models, understand the dynamics of surface hydrology, or quantify solute contribution, including nutrients and pollutants. This study aims to evaluate groundwater discharge into two small streams in the Atlantic coastal area of the Argentine Pampa Plain and ascertaining the relationship between groundwater age and 222Rn as a discharge indicator. In August 2017, samples were collected from two streams, La Ballenera and Vivoratá, and 222Rn activities, CFCs concentration, ionic composition, EC, and stable isotopes (δ18O, δ2H) were measured. The chemical composition and EC of the two streams was similar. The isotopic composition showed some difference in the Vivoratá stream among the samples close to the headwaters, which are more depleted and have a deuterium excess value similar to the ordinate of the local meteoric line. This behavior is consistent with the dominance of baseflow, with values most depleted appearing upstream and the most enriched being downstream as a result of slight evaporation from the course. The three samples in La Ballenera stream are closer to the global meteoric line, although greater enrichment is observed upstream. 222Rn concentrations were in the range of 1–2.5 Bq.L−1 in both. In the Vivoratá stream, 222Rn showed a tendency to increase in the downstream direction of the flow, while in the La Ballenera stream this trend was observed in the first two sections. CFCs values were significantly homogeneous at all sites. For most of the samples, the recharge year was set between 1987 and 1990, resulting in transit times in the order of 30 years. Combining field measurements of streamflow and EC, laboratory analyses of stable isotopes, CFCs and 222Rn, provides an efficient method for estimating groundwater discharge rates, which is ascertained by water apparent ages.

On the visual detection of non-natural records in streamflow time series: challenges and impacts

Abstract. Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.

Mutual validation of remote hydraulic estimates and flow model simulations using UAV-borne LiDAR and deep learning-based imaging techniques

Hydraulic modeling plays a pivotal role in various river-related applications, but acquiring precise datasets can be challenging due to complex environments and limited availability of field data. To address these challenges, remote sensing approaches have shown promise, but their widespread scientific use requires validation. This study investigates the utilization of an uncrewed aerial vehicle (UAV)-integrated topo-bathymetric green LiDAR system (GLS) combined with deep learning-based space-time image velocimetry (DL-STIV) techniques to remotely analyze hydraulic properties in the lower Asahi River, Japan. The research compares streamflow measurements with flow model simulations at four transects within a 1.2 km gravel bed channel with vegetation. The results demonstrate that the DL-STIV technique provides reliable velocity estimates, with root-mean-square errors ranging from 0.014 to 0.247 m/s. When using a typical velocity conversion factor of 0.85, the DL-STIV method aligns well with model predictions, yielding coefficient of determination values between 0.850 and 0.959. However, the choice of conversion index (ranging from 0.75 to 0.95) significantly affects accuracy. The study also reveals that remotely estimated discharges are reasonably consistent with model estimates, with errors ranging from 0.026 to 11.124%. However, accuracy is influenced by factors such as the presence of obstacles, uncertainties in remotely sensed water depths, and the choice of reference values. In conclusion, this research demonstrates the suitability and potential of GLS and DL-STIV for remotely analyzing hydraulic properties in rivers. The study also points to the importance of considering potential variations and uncertainties when adopting these techniques for flow modeling and validation purposes.

Machine learning-enabled calibration of river routing model parameters

Abstract Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.